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Received March 28, 2017
Accepted June 24, 2017
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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A modified scaled variable reduced coordinate (SVRC)-quantitative structure property relationship (QSPR) model for predicting liquid viscosity of pure organic compounds
Department of Chemical and Biological Engineering, Korea University, Seoul 02841, Korea 1ChemEssen Inc., 812 8th Floor. AceHighTechCity 2-Cha, 25 Seonyu-ro 13-gil, Yeongdeungpo-gu, Seoul 07282, Korea
Korean Journal of Chemical Engineering, October 2017, 34(10), 2715-2724(10), 10.1007/s11814-017-0173-3
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Abstract
Liquid viscosity is an important physical property utilized in engineering designs for transportation and processing of fluids. However, the measurement of liquid viscosity is not always easy when the materials have toxicity and instability. In this study, a modified scaled variable reduced coordinate (SVRC)-quantitative structure property relationship (QSPR) model is suggested and analyzed in terms of its performance of prediction for liquid viscosity compared to the conventional SVRC-QSPR model and the other methods. The modification was conducted by changing the initial point from triple point to ambient temperature (293 K), and assuming that the liquid viscosity at critical temperature is 0 cP. The results reveal that the prediction performance of the modified SVRC-QSPR model is comparable to the other methods as showing 7.90% of mean absolute percentage error (MAPE) and 0.9838 of R2. In terms of both the number of components and the performance of prediction, the modified SVRC-QSPR model is superior to the conventional SVRC-QSPR model. Also, the applicability of the model is improved since the condition of the end points of the modified model is not so restrictive as the conventional SVRC-QSPR model.
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Aiken LS, West SG, Pitts SC, Multiple linear regression: Testing and interpreting interactions, Sage, CA (1991).
Ammi Y, Khaouane L, Hanini S, Korean J. Chem. Eng., 32(11), 2300 (2015)
Babaei AA, Khataee A, Ahmadpour E, Sheydaei M, Kakavandi B, Alaee Z, Korean J. Chem. Eng., 33(4), 1352 (2016)
Mohagheghian E, Zafarian-Rigaki H, Motamedi-Ghahfarrokhi Y, Hemmati-Sarapardeh A, Korean J. Chem. Eng., 32(10), 2087 (2015)
Luckas M, Lucas K, AIChE J., 32, 139 (1986)
Monnery WD, Svrcek WY, Mehrotra AK, Can. J. Chem. Eng., 73(1), 3 (1995)
Jegadeesan A, Structure-based generalized models for selected purefluid saturation properties, Oklahoma State University, M.S. Thesis (2006).
Shaver RD, New scaled-variable-reduced-coordinate framework for correlation of pure fluid saturation properties, Oklahoma State University, M.S. Thesis (1990).
Shaver R, Robinson R, Gasem K, Fluid Phase Equilib., 64, 141 (1991)
Shaver R, Robinson R, Gasem K, Fluid Phase Equilib., 78, 81 (1992)
McHugh M, Krukonis V, Supercritical fluid extraction: Principles and practice, Elsevier (2013).
Sauerbrei W, Schumacher M, Stat. Med., 11, 2093 (1992)
Steyerberg EW, Eijkemans MJ, Habbema JDF, J. Clin. Epidemiol., 52, 935 (1999)
O’brien RM, Quality & Quantity, 41, 673 (2007)
Dutt NVK, Ravikumar YVL, Rani KY, Chem. Eng. Commun., 200(12), 1600 (2013)
Zhao YS, Zhang XP, Deng LY, Zhang SJ, Comput. Chem. Eng., 92, 37 (2016)
Chen BK, Liang MJ, Wu TY, Wang HP, Fluid Phase Equilib., 350, 37 (2013)
Gardas RL, Coutinho JAP, Fluid Phase Equilib., 266(1-2), 195 (2008)